Steps to create the Graduation Percentage based on Category graph
Load the required Libraries and install the required packages
library(readxl)
library(ggrepel)
## Warning: package 'ggrepel' was built under R version 3.4.2
## Loading required package: ggplot2
library(ggplot2)
Loading the dataset
setwd("~/Desktop/Nebraska education ")
Set_4 <- read_excel("~/Desktop/Nebraska education /Set 4.xlsx")
## Warning in strptime(x, format, tz = tz): unknown timezone 'default/America/
## Chicago'
Graduation <- read_excel("Set 4.xlsx")
Grad <- Graduation
Grad[Grad == '-1'] <- NA
GradSet <- na.omit(Grad)
GradCln <- GradSet[c("Description","Graduation Count","Graduation Percentage")]
Since there were around 13 description for all the students of a particular school, I have divided the dataset into three subsets based on their description.
Creating subsets based on the category
### Have grouped "Male" and "Female" description and created a subset Gender.
Gender <- GradCln[GradCln$Description == "Male" | GradCln$Description == "Female",]
### Have grouped all the Ethnic descriptions and created a subset Ethnic.
Ethnic <- GradCln[GradCln$Description == "Ethnic7 - Hispanic" | GradCln$Description == "Ethnic7 - American Indian/Alaska Native" | GradCln$Description == "Ethnic7 - Asian" | GradCln$Description == "Ethnic7 - Black or African American" | GradCln$Description == "Ethnic7 - Native Hawaiian or Other Pacific Islander" | GradCln$Description == "Ethnic7 - Black or African American" | GradCln$Description == "Ethnic7 - White" | GradCln$Description == "Ethnic7 - Two or More Races",]
### Have grouped all theremaining descriptions and created a subset Others.
Others <- GradCln[GradCln$Description == "Students eligible for free and reduced lunch" | GradCln$Description == "Special Education Students" | GradCln$Description == "English Language Learners",]
Basic Plots and ggplot for for the category Gender
Gender <- GradCln[GradCln$Description == "Male" | GradCln$Description == "Female",]
plot(Gender$`Graduation Percentage`, type="l", col="Pink")
plot(Gender$`Graduation Count`, type="l", col="Orange")
Plot1 <- ggplot(Gender, aes(x = Description, y = `Graduation Percentage`)) + theme(legend.position = "top", axis.text = element_text(size = 6))
(PLot1 <- Plot1 + geom_point(aes(color = `Graduation Count`), alpha = 0.5, size = 1.5, position = position_jitter(width = 0.25, height = 0)))
PLot1 + scale_x_discrete(name="Gender") + scale_color_continuous(name="",low="pink", high = "black")
### This plot shows the graduation percentage and count based the Gender categories.
### From the graph we can come to a conclusion that the "Male" and "Females" are almost but the males have a slightly high graduation percentage and count.
Basic Plots and ggplot for for the category Ethnic
Ethnic <- GradCln[GradCln$Description == "Ethnic7 - Hispanic" | GradCln$Description == "Ethnic7 - American Indian/Alaska Native" | GradCln$Description == "Ethnic7 - Asian" | GradCln$Description == "Ethnic7 - Black or African American" | GradCln$Description == "Ethnic7 - Native Hawaiian or Other Pacific Islander" | GradCln$Description == "Ethnic7 - Black or African American" | GradCln$Description == "Ethnic7 - White" | GradCln$Description == "Ethnic7 - Two or More Races",]
plot(Ethnic$`Graduation Percentage`, type="b", col="Orange")
boxplot(Ethnic$`Graduation Percentage`~Ethnic$Description)
ggplot(Ethnic,aes(x=`Graduation Count`))+ geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
ggplot(Ethnic,aes(x=`Graduation Percentage`))+ geom_histogram()
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
Plot2 <- ggplot(Ethnic, aes(x = Description, y = `Graduation Percentage`)) + theme(legend.position = "top", axis.text = element_text(size = 6))
(PLot3 <- Plot2 + geom_point(aes(color = `Graduation Count`), alpha = 0.5, size = 1.5, position = position_jitter(width = 0.25, height = 0)))
PLot3 + scale_x_discrete(name="Ethnicity") + scale_color_continuous(low="blue", high = "red")
### This plot shows the graduation percentage and count based on the the Ethnic categories.
### From the graph we can come to a conclusion that the "Ethnic : Whites" have the highest graduation percentage and count.
Basic Plots and ggplot for for the category Others
Others <- GradCln[GradCln$Description == "Students eligible for free and reduced lunch" | GradCln$Description == "Special Education Students" | GradCln$Description == "English Language Learners",]
plot(Others$`Graduation Percentage`, type="p", col="Brown")
plot(Others$`Graduation Count`, type="l", col="Brown")
Plot5 <- ggplot(Others, aes(x = `Graduation Percentage`, y = `Graduation Count`))
Plot5 + geom_line(aes(color = Description))
Plot5 <- ggplot(Others, aes(x = Description, y = `Graduation Percentage`)) + theme(legend.position = "top", axis.text = element_text(size = 6))
(Plot6 <- Plot5 + geom_point(aes(color = `Graduation Count`), alpha = 0.5, size = 1.5, position = position_jitter(width = 0.25, height = 0)))
Plot6 + scale_x_discrete(name="Other Category") + scale_color_continuous(name="",low="Orange", high = "Black")
### This plot shows the graduation percentage and count based the Other categories.
###From the graph we can come to a conclusion that the "Students eligible for free and reduced lunch" have the highest graduation percentage and count.